Overview

Dataset statistics

Number of variables21
Number of observations285
Missing cells733
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.9 KiB
Average record size in memory168.5 B

Variable types

Numeric5
Unsupported1
Categorical12
Text3

Alerts

offers.priceCurrency has constant value ""Constant
offers.addressCountry has constant value ""Constant
post_id is highly overall correlated with fuel and 9 other fieldsHigh correlation
offers.postalCode is highly overall correlated with transmission and 2 other fieldsHigh correlation
fuel is highly overall correlated with post_idHigh correlation
type is highly overall correlated with post_id and 1 other fieldsHigh correlation
size is highly overall correlated with post_id and 1 other fieldsHigh correlation
cylinders is highly overall correlated with post_idHigh correlation
drive is highly overall correlated with post_idHigh correlation
title status is highly overall correlated with post_id and 1 other fieldsHigh correlation
transmission is highly overall correlated with post_id and 1 other fieldsHigh correlation
paint color is highly overall correlated with post_id and 1 other fieldsHigh correlation
condition is highly overall correlated with post_id and 1 other fieldsHigh correlation
offers.addressRegion is highly overall correlated with post_id and 1 other fieldsHigh correlation
fuel is highly imbalanced (73.1%)Imbalance
title status is highly imbalanced (86.8%)Imbalance
transmission is highly imbalanced (76.6%)Imbalance
VIN has 214 (75.1%) missing valuesMissing
type has 82 (28.8%) missing valuesMissing
size has 159 (55.8%) missing valuesMissing
cylinders has 73 (25.6%) missing valuesMissing
drive has 83 (29.1%) missing valuesMissing
title status has 6 (2.1%) missing valuesMissing
paint color has 69 (24.2%) missing valuesMissing
condition has 47 (16.5%) missing valuesMissing
post_id has unique valuesUnique
post_datetime is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-02 20:41:56.580763
Analysis finished2023-08-02 20:42:18.213573
Duration21.63 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

post_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct285
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6500336 × 109
Minimum7.649984 × 109
Maximum7.6501031 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:18.266801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.649984 × 109
5-th percentile7.6499882 × 109
Q17.6500011 × 109
median7.6500265 × 109
Q37.6500664 × 109
95-th percentile7.6500947 × 109
Maximum7.6501031 × 109
Range119089
Interquartile range (IQR)65267

Descriptive statistics

Standard deviation35084.37
Coefficient of variation (CV)4.586172 × 10-6
Kurtosis-1.193866
Mean7.6500336 × 109
Median Absolute Deviation (MAD)28658
Skewness0.3648609
Sum2.1802596 × 1012
Variance1.230913 × 109
MonotonicityNot monotonic
2023-08-02T15:42:18.362988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7649983974 1
 
0.4%
7650052424 1
 
0.4%
7650057331 1
 
0.4%
7650056174 1
 
0.4%
7650054243 1
 
0.4%
7650053982 1
 
0.4%
7650053839 1
 
0.4%
7650053763 1
 
0.4%
7650052229 1
 
0.4%
7650057682 1
 
0.4%
Other values (275) 275
96.5%
ValueCountFrequency (%)
7649983974 1
0.4%
7649983985 1
0.4%
7649984092 1
0.4%
7649984236 1
0.4%
7649984275 1
0.4%
7649984571 1
0.4%
7649984580 1
0.4%
7649985980 1
0.4%
7649986024 1
0.4%
7649986056 1
0.4%
ValueCountFrequency (%)
7650103063 1
0.4%
7650102263 1
0.4%
7650102101 1
0.4%
7650101253 1
0.4%
7650098435 1
0.4%
7650098215 1
0.4%
7650097848 1
0.4%
7650097594 1
0.4%
7650097179 1
0.4%
7650096639 1
0.4%

post_datetime
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.4 KiB

offers.price
Real number (ℝ)

Distinct185
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12097.382
Minimum1
Maximum68000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:18.458821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile675.4
Q13950
median8000
Q315495
95-th percentile35980
Maximum68000
Range67999
Interquartile range (IQR)11545

Descriptive statistics

Standard deviation12465.583
Coefficient of variation (CV)1.0304364
Kurtosis5.929998
Mean12097.382
Median Absolute Deviation (MAD)5105
Skewness2.2297452
Sum3447754
Variance1.5539077 × 108
MonotonicityNot monotonic
2023-08-02T15:42:18.543226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5900 7
 
2.5%
14900 6
 
2.1%
3000 5
 
1.8%
4000 5
 
1.8%
2800 5
 
1.8%
4900 5
 
1.8%
13500 4
 
1.4%
8500 4
 
1.4%
12500 4
 
1.4%
4500 4
 
1.4%
Other values (175) 236
82.8%
ValueCountFrequency (%)
1 2
0.7%
215 1
 
0.4%
221 1
 
0.4%
242 1
 
0.4%
280 1
 
0.4%
336 3
1.1%
344 1
 
0.4%
407 1
 
0.4%
472 1
 
0.4%
600 1
 
0.4%
ValueCountFrequency (%)
68000 1
0.4%
67000 1
0.4%
65000 1
0.4%
61900 2
0.7%
60000 1
0.4%
59500 1
0.4%
50000 1
0.4%
47900 1
0.4%
44000 1
0.4%
42999 1
0.4%

offers.priceCurrency
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
USD
285 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters855
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 285
100.0%

Length

2023-08-02T15:42:18.624453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:18.690593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 285
100.0%

Most occurring characters

ValueCountFrequency (%)
U 285
33.3%
S 285
33.3%
D 285
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 855
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 285
33.3%
S 285
33.3%
D 285
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 855
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 285
33.3%
S 285
33.3%
D 285
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 285
33.3%
S 285
33.3%
D 285
33.3%

year
Real number (ℝ)

Distinct48
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.3158
Minimum1940
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:18.757831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1940
5-th percentile1984
Q12005
median2010
Q32016
95-th percentile2020.8
Maximum2024
Range84
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.034806
Coefficient of variation (CV)0.0059924867
Kurtosis8.9193895
Mean2008.3158
Median Absolute Deviation (MAD)5
Skewness-2.5155421
Sum572370
Variance144.83655
MonotonicityNot monotonic
2023-08-02T15:42:18.843071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2015 22
 
7.7%
2019 18
 
6.3%
2010 17
 
6.0%
2006 17
 
6.0%
2011 16
 
5.6%
2007 16
 
5.6%
2016 13
 
4.6%
2018 13
 
4.6%
2005 11
 
3.9%
2004 11
 
3.9%
Other values (38) 131
46.0%
ValueCountFrequency (%)
1940 2
0.7%
1963 1
0.4%
1967 1
0.4%
1968 1
0.4%
1969 1
0.4%
1970 1
0.4%
1971 1
0.4%
1972 1
0.4%
1973 1
0.4%
1974 1
0.4%
ValueCountFrequency (%)
2024 1
 
0.4%
2023 3
 
1.1%
2022 3
 
1.1%
2021 8
 
2.8%
2020 4
 
1.4%
2019 18
6.3%
2018 13
4.6%
2017 11
3.9%
2016 13
4.6%
2015 22
7.7%

yr_make_model
Text

Distinct281
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:18.979077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length31
Mean length21.996491
Min length10

Characters and Unicode

Total characters6269
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)97.2%

Sample

1st row2008 jeep wrangler unlimited sport
2nd row2016 dodge ram promaster
3rd row2015 chevy silverado 1500
4th row2015 Jeep Renegade Trailhawk 4x4
5th row2019 FORD TRANSIT T250
ValueCountFrequency (%)
ford 44
 
4.0%
2015 23
 
2.1%
honda 20
 
1.8%
chevrolet 20
 
1.8%
chevy 20
 
1.8%
2019 19
 
1.7%
2010 18
 
1.6%
2007 18
 
1.6%
4x4 17
 
1.5%
dodge 17
 
1.5%
Other values (339) 891
80.5%
2023-08-02T15:42:19.227238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
822
 
13.1%
0 503
 
8.0%
e 373
 
5.9%
2 328
 
5.2%
a 324
 
5.2%
r 312
 
5.0%
o 271
 
4.3%
1 233
 
3.7%
d 194
 
3.1%
t 178
 
2.8%
Other values (56) 2731
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3382
53.9%
Decimal Number 1484
23.7%
Space Separator 822
 
13.1%
Uppercase Letter 556
 
8.9%
Dash Punctuation 19
 
0.3%
Other Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 373
 
11.0%
a 324
 
9.6%
r 312
 
9.2%
o 271
 
8.0%
d 194
 
5.7%
t 178
 
5.3%
c 177
 
5.2%
i 176
 
5.2%
n 173
 
5.1%
l 169
 
5.0%
Other values (16) 1035
30.6%
Uppercase Letter
ValueCountFrequency (%)
E 56
 
10.1%
C 44
 
7.9%
S 43
 
7.7%
D 40
 
7.2%
R 36
 
6.5%
L 33
 
5.9%
O 32
 
5.8%
A 30
 
5.4%
T 29
 
5.2%
F 26
 
4.7%
Other values (16) 187
33.6%
Decimal Number
ValueCountFrequency (%)
0 503
33.9%
2 328
22.1%
1 233
15.7%
5 94
 
6.3%
9 80
 
5.4%
4 68
 
4.6%
6 51
 
3.4%
3 47
 
3.2%
7 44
 
3.0%
8 36
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
& 1
 
16.7%
Space Separator
ValueCountFrequency (%)
822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3938
62.8%
Common 2331
37.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 373
 
9.5%
a 324
 
8.2%
r 312
 
7.9%
o 271
 
6.9%
d 194
 
4.9%
t 178
 
4.5%
c 177
 
4.5%
i 176
 
4.5%
n 173
 
4.4%
l 169
 
4.3%
Other values (42) 1591
40.4%
Common
ValueCountFrequency (%)
822
35.3%
0 503
21.6%
2 328
 
14.1%
1 233
 
10.0%
5 94
 
4.0%
9 80
 
3.4%
4 68
 
2.9%
6 51
 
2.2%
3 47
 
2.0%
7 44
 
1.9%
Other values (4) 61
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
822
 
13.1%
0 503
 
8.0%
e 373
 
5.9%
2 328
 
5.2%
a 324
 
5.2%
r 312
 
5.0%
o 271
 
4.3%
1 233
 
3.7%
d 194
 
3.1%
t 178
 
2.8%
Other values (56) 2731
43.6%

VIN
Text

MISSING 

Distinct71
Distinct (%)100.0%
Missing214
Missing (%)75.1%
Memory size2.4 KiB
2023-08-02T15:42:19.352974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.887324
Min length13

Characters and Unicode

Total characters1199
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)100.0%

Sample

1st rowZACCJBCT0FPB65517
2nd row5N1DR2MM9KC615188
3rd row2C3CCAAG7KH590643
4th row1FT7W2B67GEC24602
5th row2HKRM4H56FH663538
ValueCountFrequency (%)
zaccjbct0fpb65517 1
 
1.4%
3c4pdcabxjt508999 1
 
1.4%
5n1dr2mm9kc615188 1
 
1.4%
2c3ccaag7kh590643 1
 
1.4%
1ft7w2b67gec24602 1
 
1.4%
2hkrm4h56fh663538 1
 
1.4%
1n4al11d93c420171 1
 
1.4%
1fmcu0d76akd20238 1
 
1.4%
2c3ccabg2mh520324 1
 
1.4%
3c4njdbb3nt154317 1
 
1.4%
Other values (61) 61
85.9%
2023-08-02T15:42:19.797469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 105
 
8.8%
2 80
 
6.7%
3 79
 
6.6%
0 75
 
6.3%
4 73
 
6.1%
5 70
 
5.8%
6 64
 
5.3%
7 55
 
4.6%
8 51
 
4.3%
C 51
 
4.3%
Other values (24) 496
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 696
58.0%
Uppercase Letter 503
42.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 51
 
10.1%
A 41
 
8.2%
B 40
 
8.0%
G 36
 
7.2%
F 35
 
7.0%
J 24
 
4.8%
R 22
 
4.4%
N 22
 
4.4%
D 21
 
4.2%
H 20
 
4.0%
Other values (14) 191
38.0%
Decimal Number
ValueCountFrequency (%)
1 105
15.1%
2 80
11.5%
3 79
11.4%
0 75
10.8%
4 73
10.5%
5 70
10.1%
6 64
9.2%
7 55
7.9%
8 51
7.3%
9 44
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 696
58.0%
Latin 503
42.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 51
 
10.1%
A 41
 
8.2%
B 40
 
8.0%
G 36
 
7.2%
F 35
 
7.0%
J 24
 
4.8%
R 22
 
4.4%
N 22
 
4.4%
D 21
 
4.2%
H 20
 
4.0%
Other values (14) 191
38.0%
Common
ValueCountFrequency (%)
1 105
15.1%
2 80
11.5%
3 79
11.4%
0 75
10.8%
4 73
10.5%
5 70
10.1%
6 64
9.2%
7 55
7.9%
8 51
7.3%
9 44
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105
 
8.8%
2 80
 
6.7%
3 79
 
6.6%
0 75
 
6.3%
4 73
 
6.1%
5 70
 
5.8%
6 64
 
5.3%
7 55
 
4.6%
8 51
 
4.3%
C 51
 
4.3%
Other values (24) 496
41.4%

fuel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
gas
256 
diesel
 
19
hybrid
 
4
other
 
3
electric
 
3

Length

Max length8
Median length3
Mean length3.3157895
Min length3

Characters and Unicode

Total characters945
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowdiesel
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 256
89.8%
diesel 19
 
6.7%
hybrid 4
 
1.4%
other 3
 
1.1%
electric 3
 
1.1%

Length

2023-08-02T15:42:19.895069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:19.973651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gas 256
89.8%
diesel 19
 
6.7%
hybrid 4
 
1.4%
other 3
 
1.1%
electric 3
 
1.1%

Most occurring characters

ValueCountFrequency (%)
s 275
29.1%
g 256
27.1%
a 256
27.1%
e 47
 
5.0%
i 26
 
2.8%
d 23
 
2.4%
l 22
 
2.3%
r 10
 
1.1%
h 7
 
0.7%
t 6
 
0.6%
Other values (4) 17
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 945
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 275
29.1%
g 256
27.1%
a 256
27.1%
e 47
 
5.0%
i 26
 
2.8%
d 23
 
2.4%
l 22
 
2.3%
r 10
 
1.1%
h 7
 
0.7%
t 6
 
0.6%
Other values (4) 17
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 945
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 275
29.1%
g 256
27.1%
a 256
27.1%
e 47
 
5.0%
i 26
 
2.8%
d 23
 
2.4%
l 22
 
2.3%
r 10
 
1.1%
h 7
 
0.7%
t 6
 
0.6%
Other values (4) 17
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 275
29.1%
g 256
27.1%
a 256
27.1%
e 47
 
5.0%
i 26
 
2.8%
d 23
 
2.4%
l 22
 
2.3%
r 10
 
1.1%
h 7
 
0.7%
t 6
 
0.6%
Other values (4) 17
 
1.8%

type
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)5.9%
Missing82
Missing (%)28.8%
Memory size2.4 KiB
SUV
66 
sedan
47 
truck
21 
pickup
15 
other
11 
Other values (7)
43 

Length

Max length11
Median length9
Mean length4.8669951
Min length3

Characters and Unicode

Total characters988
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowSUV
2nd rowSUV
3rd rowSUV
4th rowother
5th rowtruck

Common Values

ValueCountFrequency (%)
SUV 66
23.2%
sedan 47
16.5%
truck 21
 
7.4%
pickup 15
 
5.3%
other 11
 
3.9%
coupe 11
 
3.9%
hatchback 8
 
2.8%
convertible 8
 
2.8%
wagon 6
 
2.1%
mini-van 6
 
2.1%
Other values (2) 4
 
1.4%
(Missing) 82
28.8%

Length

2023-08-02T15:42:20.046698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suv 66
32.5%
sedan 47
23.2%
truck 21
 
10.3%
pickup 15
 
7.4%
other 11
 
5.4%
coupe 11
 
5.4%
hatchback 8
 
3.9%
convertible 8
 
3.9%
wagon 6
 
3.0%
mini-van 6
 
3.0%
Other values (2) 4
 
2.0%

Most occurring characters

ValueCountFrequency (%)
e 85
 
8.6%
a 78
 
7.9%
n 76
 
7.7%
c 71
 
7.2%
S 66
 
6.7%
V 66
 
6.7%
U 66
 
6.7%
s 48
 
4.9%
t 48
 
4.9%
u 48
 
4.9%
Other values (14) 336
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 784
79.4%
Uppercase Letter 198
 
20.0%
Dash Punctuation 6
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 85
10.8%
a 78
 
9.9%
n 76
 
9.7%
c 71
 
9.1%
s 48
 
6.1%
t 48
 
6.1%
u 48
 
6.1%
d 47
 
6.0%
k 44
 
5.6%
p 41
 
5.2%
Other values (10) 198
25.3%
Uppercase Letter
ValueCountFrequency (%)
S 66
33.3%
V 66
33.3%
U 66
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 982
99.4%
Common 6
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 85
 
8.7%
a 78
 
7.9%
n 76
 
7.7%
c 71
 
7.2%
S 66
 
6.7%
V 66
 
6.7%
U 66
 
6.7%
s 48
 
4.9%
t 48
 
4.9%
u 48
 
4.9%
Other values (13) 330
33.6%
Common
ValueCountFrequency (%)
- 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 85
 
8.6%
a 78
 
7.9%
n 76
 
7.7%
c 71
 
7.2%
S 66
 
6.7%
V 66
 
6.7%
U 66
 
6.7%
s 48
 
4.9%
t 48
 
4.9%
u 48
 
4.9%
Other values (14) 336
34.0%

size
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)3.2%
Missing159
Missing (%)55.8%
Memory size2.4 KiB
full-size
77 
mid-size
38 
compact
10 
sub-compact
 
1

Length

Max length11
Median length9
Mean length8.5555556
Min length7

Characters and Unicode

Total characters1078
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowmid-size
2nd rowcompact
3rd rowmid-size
4th rowmid-size
5th rowmid-size

Common Values

ValueCountFrequency (%)
full-size 77
27.0%
mid-size 38
 
13.3%
compact 10
 
3.5%
sub-compact 1
 
0.4%
(Missing) 159
55.8%

Length

2023-08-02T15:42:20.128009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:20.212024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
full-size 77
61.1%
mid-size 38
30.2%
compact 10
 
7.9%
sub-compact 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
l 154
14.3%
i 153
14.2%
- 116
10.8%
s 116
10.8%
z 115
10.7%
e 115
10.7%
u 78
7.2%
f 77
7.1%
m 49
 
4.5%
d 38
 
3.5%
Other values (6) 67
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 962
89.2%
Dash Punctuation 116
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 154
16.0%
i 153
15.9%
s 116
12.1%
z 115
12.0%
e 115
12.0%
u 78
8.1%
f 77
8.0%
m 49
 
5.1%
d 38
 
4.0%
c 22
 
2.3%
Other values (5) 45
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 962
89.2%
Common 116
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 154
16.0%
i 153
15.9%
s 116
12.1%
z 115
12.0%
e 115
12.0%
u 78
8.1%
f 77
8.0%
m 49
 
5.1%
d 38
 
4.0%
c 22
 
2.3%
Other values (5) 45
 
4.7%
Common
ValueCountFrequency (%)
- 116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 154
14.3%
i 153
14.2%
- 116
10.8%
s 116
10.8%
z 115
10.7%
e 115
10.7%
u 78
7.2%
f 77
7.1%
m 49
 
4.5%
d 38
 
3.5%
Other values (6) 67
6.2%

odometer
Real number (ℝ)

Distinct240
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180355.33
Minimum30
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:20.293337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile19078.8
Q179000
median129000
Q3173880
95-th percentile246655.4
Maximum10000000
Range9999970
Interquartile range (IQR)94880

Descriptive statistics

Standard deviation605647.91
Coefficient of variation (CV)3.3580815
Kurtosis245.80765
Mean180355.33
Median Absolute Deviation (MAD)47000
Skewness15.230205
Sum51401270
Variance3.6680939 × 1011
MonotonicityNot monotonic
2023-08-02T15:42:20.388655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148000 5
 
1.8%
95000 4
 
1.4%
200000 4
 
1.4%
125000 3
 
1.1%
100000 3
 
1.1%
115000 3
 
1.1%
122000 3
 
1.1%
129102 3
 
1.1%
160000 3
 
1.1%
150000 3
 
1.1%
Other values (230) 251
88.1%
ValueCountFrequency (%)
30 1
0.4%
125 1
0.4%
170 1
0.4%
289 2
0.7%
1000 1
0.4%
6734 1
0.4%
9740 1
0.4%
9999 1
0.4%
10500 1
0.4%
10895 1
0.4%
ValueCountFrequency (%)
10000000 1
0.4%
2000000 1
0.4%
1000000 1
0.4%
898989 1
0.4%
720000 1
0.4%
698000 1
0.4%
645000 1
0.4%
630000 1
0.4%
605000 1
0.4%
500000 1
0.4%

cylinders
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)1.9%
Missing73
Missing (%)25.6%
Memory size2.4 KiB
6 cylinders
94 
4 cylinders
65 
8 cylinders
52 
other
 
1

Length

Max length11
Median length11
Mean length10.971698
Min length5

Characters and Unicode

Total characters2326
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row6 cylinders
2nd row6 cylinders
3rd row4 cylinders
4th row6 cylinders
5th row6 cylinders

Common Values

ValueCountFrequency (%)
6 cylinders 94
33.0%
4 cylinders 65
22.8%
8 cylinders 52
18.2%
other 1
 
0.4%
(Missing) 73
25.6%

Length

2023-08-02T15:42:20.468772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:20.551292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cylinders 211
49.9%
6 94
22.2%
4 65
 
15.4%
8 52
 
12.3%
other 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 212
9.1%
r 212
9.1%
211
9.1%
c 211
9.1%
y 211
9.1%
l 211
9.1%
i 211
9.1%
n 211
9.1%
d 211
9.1%
s 211
9.1%
Other values (6) 214
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1904
81.9%
Space Separator 211
 
9.1%
Decimal Number 211
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 212
11.1%
r 212
11.1%
c 211
11.1%
y 211
11.1%
l 211
11.1%
i 211
11.1%
n 211
11.1%
d 211
11.1%
s 211
11.1%
o 1
 
0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
6 94
44.5%
4 65
30.8%
8 52
24.6%
Space Separator
ValueCountFrequency (%)
211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1904
81.9%
Common 422
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 212
11.1%
r 212
11.1%
c 211
11.1%
y 211
11.1%
l 211
11.1%
i 211
11.1%
n 211
11.1%
d 211
11.1%
s 211
11.1%
o 1
 
0.1%
Other values (2) 2
 
0.1%
Common
ValueCountFrequency (%)
211
50.0%
6 94
22.3%
4 65
 
15.4%
8 52
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 212
9.1%
r 212
9.1%
211
9.1%
c 211
9.1%
y 211
9.1%
l 211
9.1%
i 211
9.1%
n 211
9.1%
d 211
9.1%
s 211
9.1%
Other values (6) 214
9.2%

drive
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)1.5%
Missing83
Missing (%)29.1%
Memory size2.4 KiB
4wd
107 
fwd
66 
rwd
29 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters606
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4wd
2nd rowrwd
3rd row4wd
4th row4wd
5th row4wd

Common Values

ValueCountFrequency (%)
4wd 107
37.5%
fwd 66
23.2%
rwd 29
 
10.2%
(Missing) 83
29.1%

Length

2023-08-02T15:42:20.624174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:20.704390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4wd 107
53.0%
fwd 66
32.7%
rwd 29
 
14.4%

Most occurring characters

ValueCountFrequency (%)
w 202
33.3%
d 202
33.3%
4 107
17.7%
f 66
 
10.9%
r 29
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 499
82.3%
Decimal Number 107
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 202
40.5%
d 202
40.5%
f 66
 
13.2%
r 29
 
5.8%
Decimal Number
ValueCountFrequency (%)
4 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 499
82.3%
Common 107
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 202
40.5%
d 202
40.5%
f 66
 
13.2%
r 29
 
5.8%
Common
ValueCountFrequency (%)
4 107
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 202
33.3%
d 202
33.3%
4 107
17.7%
f 66
 
10.9%
r 29
 
4.8%

title status
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)1.8%
Missing6
Missing (%)2.1%
Memory size2.4 KiB
clean
268 
rebuilt
 
6
lien
 
2
missing
 
2
salvage
 
1

Length

Max length7
Median length5
Mean length5.0573477
Min length4

Characters and Unicode

Total characters1411
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowclean
2nd rowclean
3rd rowclean
4th rowclean
5th rowclean

Common Values

ValueCountFrequency (%)
clean 268
94.0%
rebuilt 6
 
2.1%
lien 2
 
0.7%
missing 2
 
0.7%
salvage 1
 
0.4%
(Missing) 6
 
2.1%

Length

2023-08-02T15:42:20.776117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:20.855777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
clean 268
96.1%
rebuilt 6
 
2.2%
lien 2
 
0.7%
missing 2
 
0.7%
salvage 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
l 277
19.6%
e 277
19.6%
n 272
19.3%
a 270
19.1%
c 268
19.0%
i 12
 
0.9%
r 6
 
0.4%
b 6
 
0.4%
u 6
 
0.4%
t 6
 
0.4%
Other values (4) 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1411
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 277
19.6%
e 277
19.6%
n 272
19.3%
a 270
19.1%
c 268
19.0%
i 12
 
0.9%
r 6
 
0.4%
b 6
 
0.4%
u 6
 
0.4%
t 6
 
0.4%
Other values (4) 11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1411
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 277
19.6%
e 277
19.6%
n 272
19.3%
a 270
19.1%
c 268
19.0%
i 12
 
0.9%
r 6
 
0.4%
b 6
 
0.4%
u 6
 
0.4%
t 6
 
0.4%
Other values (4) 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 277
19.6%
e 277
19.6%
n 272
19.3%
a 270
19.1%
c 268
19.0%
i 12
 
0.9%
r 6
 
0.4%
b 6
 
0.4%
u 6
 
0.4%
t 6
 
0.4%
Other values (4) 11
 
0.8%

transmission
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
automatic
267 
manual
 
16
other
 
2

Length

Max length9
Median length9
Mean length8.8035088
Min length5

Characters and Unicode

Total characters2509
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic 267
93.7%
manual 16
 
5.6%
other 2
 
0.7%

Length

2023-08-02T15:42:20.930301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:21.012309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
automatic 267
93.7%
manual 16
 
5.6%
other 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 566
22.6%
t 536
21.4%
u 283
11.3%
m 283
11.3%
o 269
10.7%
i 267
10.6%
c 267
10.6%
n 16
 
0.6%
l 16
 
0.6%
h 2
 
0.1%
Other values (2) 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2509
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 566
22.6%
t 536
21.4%
u 283
11.3%
m 283
11.3%
o 269
10.7%
i 267
10.6%
c 267
10.6%
n 16
 
0.6%
l 16
 
0.6%
h 2
 
0.1%
Other values (2) 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 2509
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 566
22.6%
t 536
21.4%
u 283
11.3%
m 283
11.3%
o 269
10.7%
i 267
10.6%
c 267
10.6%
n 16
 
0.6%
l 16
 
0.6%
h 2
 
0.1%
Other values (2) 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 566
22.6%
t 536
21.4%
u 283
11.3%
m 283
11.3%
o 269
10.7%
i 267
10.6%
c 267
10.6%
n 16
 
0.6%
l 16
 
0.6%
h 2
 
0.1%
Other values (2) 4
 
0.2%

paint color
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)4.6%
Missing69
Missing (%)24.2%
Memory size2.4 KiB
black
51 
white
42 
silver
30 
grey
29 
blue
23 
Other values (5)
41 

Length

Max length6
Median length5
Mean length4.7083333
Min length3

Characters and Unicode

Total characters1017
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblack
2nd rowblue
3rd rowblack
4th rowwhite
5th rowsilver

Common Values

ValueCountFrequency (%)
black 51
17.9%
white 42
14.7%
silver 30
10.5%
grey 29
10.2%
blue 23
 
8.1%
red 23
 
8.1%
brown 10
 
3.5%
green 3
 
1.1%
custom 3
 
1.1%
orange 2
 
0.7%
(Missing) 69
24.2%

Length

2023-08-02T15:42:21.080967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:21.184034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
black 51
23.6%
white 42
19.4%
silver 30
13.9%
grey 29
13.4%
blue 23
10.6%
red 23
10.6%
brown 10
 
4.6%
green 3
 
1.4%
custom 3
 
1.4%
orange 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 155
15.2%
l 104
10.2%
r 97
 
9.5%
b 84
 
8.3%
i 72
 
7.1%
c 54
 
5.3%
a 53
 
5.2%
w 52
 
5.1%
k 51
 
5.0%
t 45
 
4.4%
Other values (10) 250
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1017
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 155
15.2%
l 104
10.2%
r 97
 
9.5%
b 84
 
8.3%
i 72
 
7.1%
c 54
 
5.3%
a 53
 
5.2%
w 52
 
5.1%
k 51
 
5.0%
t 45
 
4.4%
Other values (10) 250
24.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1017
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 155
15.2%
l 104
10.2%
r 97
 
9.5%
b 84
 
8.3%
i 72
 
7.1%
c 54
 
5.3%
a 53
 
5.2%
w 52
 
5.1%
k 51
 
5.0%
t 45
 
4.4%
Other values (10) 250
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 155
15.2%
l 104
10.2%
r 97
 
9.5%
b 84
 
8.3%
i 72
 
7.1%
c 54
 
5.3%
a 53
 
5.2%
w 52
 
5.1%
k 51
 
5.0%
t 45
 
4.4%
Other values (10) 250
24.6%

condition
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)2.1%
Missing47
Missing (%)16.5%
Memory size2.4 KiB
good
93 
excellent
82 
like new
49 
fair
12 
salvage
 
2

Length

Max length9
Median length8
Mean length6.5714286
Min length4

Characters and Unicode

Total characters1564
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowexcellent
3rd rowexcellent
4th rowgood
5th rowgood

Common Values

ValueCountFrequency (%)
good 93
32.6%
excellent 82
28.8%
like new 49
17.2%
fair 12
 
4.2%
salvage 2
 
0.7%
(Missing) 47
16.5%

Length

2023-08-02T15:42:21.277907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:21.354534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
good 93
32.4%
excellent 82
28.6%
like 49
17.1%
new 49
17.1%
fair 12
 
4.2%
salvage 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 346
22.1%
l 215
13.7%
o 186
11.9%
n 131
 
8.4%
g 95
 
6.1%
d 93
 
5.9%
x 82
 
5.2%
c 82
 
5.2%
t 82
 
5.2%
i 61
 
3.9%
Other values (8) 191
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1515
96.9%
Space Separator 49
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 346
22.8%
l 215
14.2%
o 186
12.3%
n 131
 
8.6%
g 95
 
6.3%
d 93
 
6.1%
x 82
 
5.4%
c 82
 
5.4%
t 82
 
5.4%
i 61
 
4.0%
Other values (7) 142
9.4%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1515
96.9%
Common 49
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 346
22.8%
l 215
14.2%
o 186
12.3%
n 131
 
8.6%
g 95
 
6.3%
d 93
 
6.1%
x 82
 
5.4%
c 82
 
5.4%
t 82
 
5.4%
i 61
 
4.0%
Other values (7) 142
9.4%
Common
ValueCountFrequency (%)
49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 346
22.1%
l 215
13.7%
o 186
11.9%
n 131
 
8.4%
g 95
 
6.1%
d 93
 
5.9%
x 82
 
5.2%
c 82
 
5.2%
t 82
 
5.2%
i 61
 
3.9%
Other values (8) 191
12.2%

offers.addressRegion
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
IL
95 
MI
94 
OH
45 
WI
37 
IN
14 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters570
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWI
2nd rowMI
3rd rowWI
4th rowMI
5th rowMI

Common Values

ValueCountFrequency (%)
IL 95
33.3%
MI 94
33.0%
OH 45
15.8%
WI 37
 
13.0%
IN 14
 
4.9%

Length

2023-08-02T15:42:21.423174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:21.498657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
il 95
33.3%
mi 94
33.0%
oh 45
15.8%
wi 37
 
13.0%
in 14
 
4.9%

Most occurring characters

ValueCountFrequency (%)
I 240
42.1%
L 95
 
16.7%
M 94
 
16.5%
O 45
 
7.9%
H 45
 
7.9%
W 37
 
6.5%
N 14
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 570
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 240
42.1%
L 95
 
16.7%
M 94
 
16.5%
O 45
 
7.9%
H 45
 
7.9%
W 37
 
6.5%
N 14
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 240
42.1%
L 95
 
16.7%
M 94
 
16.5%
O 45
 
7.9%
H 45
 
7.9%
W 37
 
6.5%
N 14
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 240
42.1%
L 95
 
16.7%
M 94
 
16.5%
O 45
 
7.9%
H 45
 
7.9%
W 37
 
6.5%
N 14
 
2.5%

offers.addressLocality
Text

Distinct132
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:21.644758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.354386
Min length4

Characters and Unicode

Total characters2666
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)31.2%

Sample

1st rowCedarburg
2nd rowSterling Heights
3rd rowWaukesha
4th rowMount Clemens
5th rowFlint
ValueCountFrequency (%)
oak 20
 
5.1%
milwaukee 17
 
4.3%
chicago 17
 
4.3%
forest 16
 
4.1%
waukesha 14
 
3.6%
ortonville 13
 
3.3%
creek 13
 
3.3%
battle 12
 
3.0%
park 9
 
2.3%
new 9
 
2.3%
Other values (145) 254
64.5%
2023-08-02T15:42:21.896923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 279
 
10.5%
a 234
 
8.8%
r 173
 
6.5%
o 171
 
6.4%
l 166
 
6.2%
i 155
 
5.8%
n 154
 
5.8%
t 137
 
5.1%
k 115
 
4.3%
109
 
4.1%
Other values (36) 973
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2163
81.1%
Uppercase Letter 394
 
14.8%
Space Separator 109
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 279
12.9%
a 234
10.8%
r 173
 
8.0%
o 171
 
7.9%
l 166
 
7.7%
i 155
 
7.2%
n 154
 
7.1%
t 137
 
6.3%
k 115
 
5.3%
s 107
 
4.9%
Other values (13) 472
21.8%
Uppercase Letter
ValueCountFrequency (%)
C 81
20.6%
O 38
9.6%
B 35
8.9%
M 31
 
7.9%
W 26
 
6.6%
S 23
 
5.8%
F 22
 
5.6%
L 19
 
4.8%
A 17
 
4.3%
H 17
 
4.3%
Other values (12) 85
21.6%
Space Separator
ValueCountFrequency (%)
109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2557
95.9%
Common 109
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 279
 
10.9%
a 234
 
9.2%
r 173
 
6.8%
o 171
 
6.7%
l 166
 
6.5%
i 155
 
6.1%
n 154
 
6.0%
t 137
 
5.4%
k 115
 
4.5%
s 107
 
4.2%
Other values (35) 866
33.9%
Common
ValueCountFrequency (%)
109
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 279
 
10.5%
a 234
 
8.8%
r 173
 
6.5%
o 171
 
6.4%
l 166
 
6.2%
i 155
 
5.8%
n 154
 
5.8%
t 137
 
5.1%
k 115
 
4.3%
109
 
4.1%
Other values (36) 973
36.5%

offers.postalCode
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52396.632
Minimum43035
Maximum60804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-08-02T15:42:22.004420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43035
5-th percentile44135
Q148113
median49311
Q360139
95-th percentile60616.6
Maximum60804
Range17769
Interquartile range (IQR)12026

Descriptive statistics

Standard deviation6129.1972
Coefficient of variation (CV)0.11697693
Kurtosis-1.5008744
Mean52396.632
Median Absolute Deviation (MAD)3905
Skewness0.27960005
Sum14933040
Variance37567058
MonotonicityNot monotonic
2023-08-02T15:42:22.092426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60452 16
 
5.6%
48462 13
 
4.6%
49037 12
 
4.2%
53188 9
 
3.2%
45344 7
 
2.5%
44320 6
 
2.1%
45826 6
 
2.1%
53187 5
 
1.8%
48036 5
 
1.8%
44871 4
 
1.4%
Other values (161) 202
70.9%
ValueCountFrequency (%)
43035 1
0.4%
43311 1
0.4%
43551 1
0.4%
43558 1
0.4%
43560 2
0.7%
43611 1
0.4%
43614 1
0.4%
43623 1
0.4%
44017 1
0.4%
44095 1
0.4%
ValueCountFrequency (%)
60804 2
0.7%
60659 1
 
0.4%
60651 2
0.7%
60641 3
1.1%
60640 1
 
0.4%
60630 1
 
0.4%
60629 3
1.1%
60622 1
 
0.4%
60618 1
 
0.4%
60611 1
 
0.4%

offers.addressCountry
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
US
285 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 285
100.0%

Length

2023-08-02T15:42:22.175044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:42:22.241117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
us 285
100.0%

Most occurring characters

ValueCountFrequency (%)
U 285
50.0%
S 285
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 570
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 285
50.0%
S 285
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 285
50.0%
S 285
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 285
50.0%
S 285
50.0%

Interactions

2023-08-02T15:42:13.886441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:41:57.338928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:02.371675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:06.659115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:09.734164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:15.135944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:41:58.534020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:03.553549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:07.540815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:10.874475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:15.867542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:41:59.718815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:04.322207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:08.107397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:11.713011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:16.241312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:00.330012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:04.980749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:08.305883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:12.182549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:17.102122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:01.428153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:05.902619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:08.991780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:42:13.110655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-02T15:42:22.294225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
post_idoffers.priceyearodometeroffers.postalCodefueltypesizecylindersdrivetitle statustransmissionpaint colorconditionoffers.addressRegion
post_id1.0000.063-0.259-0.043-0.0221.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
offers.price0.0631.000-0.166-0.1720.0080.4280.2370.0000.3520.1700.4490.0560.0000.3690.234
year-0.259-0.1661.0000.2070.2530.3580.2420.4530.3810.3890.3180.1380.4080.3600.269
odometer-0.043-0.1720.2071.0000.0330.2550.1340.2350.2590.1410.2810.2660.1830.0000.219
offers.postalCode-0.0220.0080.2530.0331.0000.3300.2370.3850.0000.2520.3980.5530.2150.5250.638
fuel1.0000.4280.3580.2550.3301.0000.2930.0000.0940.1060.0000.0000.0000.0000.000
type1.0000.2370.2420.1340.2370.2931.0000.3650.2900.4340.5180.2710.1350.3760.164
size1.0000.0000.4530.2350.3850.0000.3651.0000.3880.2220.0000.0940.5670.2500.000
cylinders1.0000.3520.3810.2590.0000.0940.2900.3881.0000.3340.1260.0470.1690.1120.008
drive1.0000.1700.3890.1410.2520.1060.4340.2220.3341.0000.1570.0580.2260.0590.174
title status1.0000.4490.3180.2810.3980.0000.5180.0000.1260.1571.0000.1780.0000.2690.031
transmission1.0000.0560.1380.2660.5530.0000.2710.0940.0470.0580.1781.0000.1850.1420.027
paint color1.0000.0000.4080.1830.2150.0000.1350.5670.1690.2260.0000.1851.0000.1860.163
condition1.0000.3690.3600.0000.5250.0000.3760.2500.1120.0590.2690.1420.1861.0000.239
offers.addressRegion1.0000.2340.2690.2190.6380.0000.1640.0000.0080.1740.0310.0270.1630.2391.000

Missing values

2023-08-02T15:42:17.793842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-02T15:42:18.001892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-02T15:42:18.137587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

post_idpost_datetimeoffers.priceoffers.priceCurrencyyearyr_make_modelVINfueltypesizeodometercylindersdrivetitle statustransmissionpaint colorconditionoffers.addressRegionoffers.addressLocalityoffers.postalCodeoffers.addressCountry
076499839742023-08-01 15:54:18-05:008800.00USD20082008 jeep wrangler unlimited sportNaNgasSUVmid-size1963006 cylinders4wdcleanautomaticblackgoodWICedarburg53012US
176499839852023-08-01 16:54:20-04:0010500.00USD20162016 dodge ram promasterNaNdieselNaNNaN244000NaNNaNcleanautomaticNaNNaNMISterling Heights48310US
276499840922023-08-01 15:54:37-05:009995.00USD20152015 chevy silverado 1500NaNgasNaNNaN1951096 cylindersrwdcleanautomaticblueNaNWIWaukesha53187US
376499842362023-08-01 16:55:04-04:0015495.00USD20152015 Jeep Renegade Trailhawk 4x4ZACCJBCT0FPB65517gasSUVcompact909104 cylinders4wdcleanautomaticblackexcellentMIMount Clemens48043US
476499842752023-08-01 16:55:11-04:0030800.00USD20192019 FORD TRANSIT T250NaNgasNaNNaN102400NaNNaNcleanautomaticwhiteexcellentMIFlint48507US
576499845712023-08-01 16:55:52-04:0014900.00USD20172017 Dodge Journey GT AWDNaNgasNaNNaN691176 cylinders4wdcleanautomaticsilvergoodMIOrtonville48462US
676499845802023-08-01 16:55:54-04:005900.00USD20082008 bmw x5 awdNaNgasNaNNaN1535176 cylinders4wdcleanautomaticbluegoodMIOrtonville48462US
776499859802023-08-01 16:59:31-04:004995.00USD20092009 Ford Escape XLTNaNgasSUVmid-size1700004 cylindersfwdcleanmanualblackexcellentOHBucyrus44820US
876499860242023-08-01 15:59:37-05:00280.00USD20192019 2019 Nissan Pathfinder5N1DR2MM9KC615188gasotherNaN901816 cylindersNaNcleanautomaticwhitelike newILLa Grange Park60526US
976499860562023-08-01 16:59:42-04:001500.00USD20012001 2001 ford ranger xltNaNgastruckmid-size2802006 cylinders4wdcleanautomaticgreygoodOHPerrysburg43551US
post_idpost_datetimeoffers.priceoffers.priceCurrencyyearyr_make_modelVINfueltypesizeodometercylindersdrivetitle statustransmissionpaint colorconditionoffers.addressRegionoffers.addressLocalityoffers.postalCodeoffers.addressCountry
27576500966392023-08-01 23:34:07-04:006350.00USD20012001 ford f250NaNgastruckfull-size950008 cylinders4wdcleanautomaticredgoodOHNew Carlisle45344US
27676500971792023-08-01 22:37:30-05:004200.00USD20062006 Toyota Corolla SNaNgassedanNaN100000NaNNaNcleanautomaticredexcellentILElk Grove Village60007US
27776500975942023-08-01 23:40:09-04:0013500.00USD19731973 chevrolet novaNaNgascoupeNaN99998 cylindersrwdcleanautomaticblackgoodMIIonia48846US
27876500978482023-08-01 22:41:37-05:0018000.00USD19721972 plymouth road runnerNaNgasNaNNaN123458 cylindersrwdmissingautomaticbluegoodWIKewaskum53040US
27976500982152023-08-01 23:43:51-04:006350.00USD20012001 Ford F250NaNgastruckfull-size950008 cylinders4wdcleanautomaticredgoodOHNew Carlisle45344US
28076500984352023-08-01 22:45:14-05:002000.00USD20022002 buick lesabreNaNgassedanfull-size177000NaNfwdcleanautomaticbluegoodWIMilwaukee53224US
28176501012532023-08-01 23:04:33-05:003965.00USD20022002 toyota camry xleNaNgasNaNfull-size1040006 cylindersNaNcleanautomaticbrownlike newILChicago60629US
28276501021012023-08-01 23:10:44-05:008600.00USD20122012 toyota priusNaNhybridNaNNaN163000NaNNaNcleanautomaticwhitegoodILNorthbrook60062US
28376501022632023-08-02 00:11:57-04:0018600.00USD20152015 subaru foresterNaNgasotherfull-size815006 cylinders4wdrebuiltautomaticbrownexcellentMITraverse City49685US
28476501030632023-08-02 00:17:39-04:006000.00USD20062006 saturn vueNaNgasSUVcompact1540004 cylindersfwdcleanmanualredexcellentMILowell49331US